Our experiments show that the proposed video imprint aggregation yields better performances in both the supervised and unsupervised tasks than existing algorithms. The video imprint also allows for the reasoning over the spatial layouts of features across frames. Inspired by the attention mechanism in memory networks [1]} reasoning over sentences in priming text and video face recognition [2]}, the proposed reasoning network analyzes evidences at different spatial locations of the compact video imprint while carrying out the recognition task, which also highlights the key areas of the imprint. These key areas of the imprint could readily be mapped back to specific video frames and corresponding spatial locations. In this way, event recounting is implemented as an integral part of recognition instead of a post-processing step.
